Data-Efficient Framework for Personalized Physiotherapy Feedback
| Autor: | Tomoya Tamei, Kazushi Ikeda, Bryan Lao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Activities of daily living Sit to stand Computer science Process (engineering) telehealth latent variable model 020207 software engineering 02 engineering and technology Telehealth lcsh:QA75.5-76.95 Task (project management) sit-to-stand symbols.namesake Augmented feedback 0202 electrical engineering electronic engineering information engineering Physical therapy medicine symbols 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Latent variable model Gaussian process physiotherapy |
| Zdroj: | Frontiers in Computer Science, Vol 2 (2020) |
| ISSN: | 2624-9898 |
| DOI: | 10.3389/fcomp.2020.00003/full |
| Popis: | Physiotherapy is a labor-intensive process that has become increasingly inaccessible. Existing telehealth solutions overcome many of the logistical problems, but they are cumbersome to re-calibrate for the various exercises involved. To facilitate self-exercise efficiently, we developed a framework for personalized physiotherapy exercises. Our approach eliminates the need to re-calibrate for different exercises, using only few user-specific demonstrations available during collocated therapy. Two types of augmented feedback are available to the user for self-correction. The framework's utility was demonstrated for the sit-to-stand task, an important activity of daily living. Although further testing is necessary, our results suggest that the framework can be generalized to the learning of arbitrary motor behaviors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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